Toward sustainable harvest strategies for marine fisheries that include recreational fishing
Bibliographic record
Abstract
Abstract Recreational fishing (RF) is a large yet undervalued component of fisheries globally. While progress has been made in monitoring, assessing, and managing the sector in isolation, integration of RF into the management of multi‐sector fisheries has been limited, particularly relative to the commercial sector. This marginalises recreational fishers and reduces the likelihood of achieving the sector's objectives and, more broadly, achieving fisheries sustainability. We examined the nature and extent of RF inclusion in harvest strategies (HSs) for marine fisheries across 15 regions in 11 nations to define the gap in inclusion that has developed between sectors. We focused on high‐income nations with a high level of RF governance and used a questionnaire to elicit expert knowledge on HSs due to the paucity of published documents. In total, 339 HSs were considered. We found that RF inclusion in HSs was more similar to the small‐scale sector (i.e., artisanal, cultural, or subsistence) than the commercial sector, with explicit operational objectives, data collection, performance indicators, reference points, and management controls lacking in many regions. Where specified, RF objectives focused on sustainability, economic value and catch allocation rather than directly relating to the recreational fishing experience. Conflicts with other sectors included competition with the commercial sector for limited resources, highlighting the importance of equitable resource allocation policies alongside HSs. We propose that RF be explicitly incorporated into HSs to ensure fisheries are ecologically, economically, and socially sustainable, and we recommend that fisheries organisations urgently review HSs for marine fisheries with a recreational component to close the harvest strategy gap among sectors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".